Specifically, we first adopt CAM to generate the pixel-level labels for the images with image-level labels. Then, we train a clean segmentation model with a small set of strong annotations to distinguish the clean labels from the noisy pixel-level labels according to the cross-entropy loss. A common method is to consider samples with smaller loss as clean ones [1]} [2]} [3]}. Subsequently, we construct a superpixel-based graph on one image by considering the dual constraints of spatial adjacency and semantic similarity, and thus we can correct noisy labels by embedding the clean labels into the graph and propagating the clean labels to the noisy labels using a Graph Attention Network (GAT) [4]}. Finally, the corrected pixel-level pseudo labels are used to train a semantic segmentation model. Compared with initial seed regions, the accuracy of our seed regions is largely improved (96.7% vs 86.7% in PASCAL VOC 2012 trainaug set).
